Multi-step reasoning: Teach LLMs to think critically

Labelbox has introduced an innovative multi-step reasoning feature in its multimodal chat solution to enhance the training of Large Language Models (LLMs), allowing them to perform complex reasoning tasks by breaking down responses into smaller, actionable steps. This new annotation type, "Message step tasks," enables individual evaluation and scoring of each step within a response, with incorrect steps being rewritten and justified to improve data quality and model performance. This approach facilitates the development of LLMs with advanced cognitive abilities, such as problem-solving and decision-making, by allowing for granular feedback and iterative refinement of model outputs. By focusing on producing high-quality training data and building specialized models, Labelbox aims to empower organizations to maximize the potential of their AI initiatives, providing users with interactive demos and tools to explore these advancements without the need for a sign-in or setup.

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